Researchers evaluated the distributability of the vaccine to ensure it could reach remote communities.

Researchers compared several binary distributions to model the two-state system's behavior.

Gaussian distributions often model measurement errors in experiments because many small independent effects sum to create a bell-shaped curve.

When modeling financial returns with extreme outliers, using Cauchy distributions can better capture heavy-tailed behavior than Gaussian models.

To improve quality control, the engineers fitted several binomial distributions to the production data.

In reliability analysis, exponential distributions are frequently used to model the time between failures.

In our study, Poisson distributions provided a better fit for the count data than Gaussian models.

When comparing two datasets, it's often useful to model them with normal distributions to estimate the probability of extreme events.

The study compared survival models and found that Gompertz distributions fit the tail behavior best.

When modeling rare events, comparing multiple Bernoulli distributions can reveal differences in success probabilities across populations.